Computational models such as Dirichlet-multinomial classification models or Bayesian networks are useful for a range of problems. For example, some modeling techniques use such computational models for classifying documents based on the contents or meanings of text strings in those documents. Such models are often trained using a corpus of documents with known classes, and can thus outperform techniques based on predetermined keyword lists or other naive classifiers. However, conventional computational models are limited in the accuracy with which they can represent training data, and thus are limited in the accuracy with which they can classify documents.